{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 984,
   "id": "d0026b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户id</th>\n",
       "      <th>消费金额</th>\n",
       "      <th>消费次数</th>\n",
       "      <th>最后一次消费时间</th>\n",
       "      <th>R(最后一次消费距提数日时间)</th>\n",
       "      <th>F(月均消费次数)</th>\n",
       "      <th>M(月均消费金额)</th>\n",
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       "      <td>Gx4sJzcQog01UhZL</td>\n",
       "      <td>300.04</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-26</td>\n",
       "      <td>24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.04</td>\n",
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       "    <tr>\n",
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       "      <td>2016-06-26</td>\n",
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       "      <td>AouXr0EOUtSRdiYK</td>\n",
       "      <td>50.00</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yds7U30hnRZDiLtb</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OFDTSXrhN9Q2mbVw</td>\n",
       "      <td>1000.03</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-06-27</td>\n",
       "      <td>23</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1000.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               用户id     消费金额  消费次数   最后一次消费时间  R(最后一次消费距提数日时间)  F(月均消费次数)  \\\n",
       "0  Gx4sJzcQog01UhZL   300.04     2 2016-06-26               24        2.0   \n",
       "1  kEXrhTiug93DIcLG   300.00     3 2016-06-26               24        3.0   \n",
       "2  AouXr0EOUtSRdiYK    50.00     4 2016-06-19               31        4.0   \n",
       "3  Yds7U30hnRZDiLtb   100.00     1 2016-06-16               34        1.0   \n",
       "4  OFDTSXrhN9Q2mbVw  1000.03    12 2016-06-27               23       12.0   \n",
       "\n",
       "   M(月均消费金额)  \n",
       "0     300.04  \n",
       "1     300.00  \n",
       "2      50.00  \n",
       "3     100.00  \n",
       "4    1000.03  "
      ]
     },
     "execution_count": 984,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "datafile = '移动公司客户信息预处理.xlsx'\n",
    "data = pd.read_excel(datafile)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 985,
   "id": "e7112fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R(最后一次消费距提数日时间)</th>\n",
       "      <th>F(月均消费次数)</th>\n",
       "      <th>M(月均消费金额)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>300.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
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       "      <td>1000.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R(最后一次消费距提数日时间)  F(月均消费次数)  M(月均消费金额)\n",
       "0               24        2.0     300.04\n",
       "1               24        3.0     300.00\n",
       "2               31        4.0      50.00\n",
       "3               34        1.0     100.00\n",
       "4               23       12.0    1000.03"
      ]
     },
     "execution_count": 985,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata = data[['R(最后一次消费距提数日时间)', 'F(月均消费次数)', 'M(月均消费金额)']]\n",
    "data.shape\n",
    "cdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 986,
   "id": "8778340c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>R(最后一次消费距提数日时间)</th>\n",
       "      <th>F(月均消费次数)</th>\n",
       "      <th>M(月均消费金额)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>用户id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gx4sJzcQog01UhZL</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>300.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kEXrhTiug93DIcLG</th>\n",
       "      <td>24</td>\n",
       "      <td>3.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>AouXr0EOUtSRdiYK</th>\n",
       "      <td>31</td>\n",
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       "      <td>23</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "                  R(最后一次消费距提数日时间)  F(月均消费次数)  M(月均消费金额)\n",
       "用户id                                                   \n",
       "Gx4sJzcQog01UhZL               24        2.0     300.04\n",
       "kEXrhTiug93DIcLG               24        3.0     300.00\n",
       "AouXr0EOUtSRdiYK               31        4.0      50.00\n",
       "Yds7U30hnRZDiLtb               34        1.0     100.00\n",
       "OFDTSXrhN9Q2mbVw               23       12.0    1000.03"
      ]
     },
     "execution_count": 986,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata.index = data['用户id']\n",
    "cdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "id": "0ab28960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R(最后一次消费距提数日时间)     24.265734\n",
       "F(月均消费次数)            1.751748\n",
       "M(月均消费金额)          103.332657\n",
       "dtype: float64"
      ]
     },
     "execution_count": 987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 988,
   "id": "026cf7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R(最后一次消费距提数日时间)     11.114682\n",
       "F(月均消费次数)            1.427025\n",
       "M(月均消费金额)          135.911360\n",
       "dtype: float64"
      ]
     },
     "execution_count": 988,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 989,
   "id": "987e7481",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>F(标准化)</th>\n",
       "      <th>M(标准化)</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>OFDTSXrhN9Q2mbVw</th>\n",
       "      <td>-0.113879</td>\n",
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      ],
      "text/plain": [
       "                    R(标准化)    F(标准化)    M(标准化)\n",
       "用户id                                          \n",
       "Gx4sJzcQog01UhZL -0.023908  0.173965  1.447321\n",
       "kEXrhTiug93DIcLG -0.023908  0.874723  1.447027\n",
       "AouXr0EOUtSRdiYK  0.605889  1.575482 -0.392408\n",
       "Yds7U30hnRZDiLtb  0.875802 -0.526794 -0.024521\n",
       "OFDTSXrhN9Q2mbVw -0.113879  7.181550  6.597663"
      ]
     },
     "execution_count": 989,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_cdata = (cdata - cdata.mean()) / cdata.std()\n",
    "z_cdata.head()\n",
    "z_cdata.columns = ['R(标准化)', 'F(标准化)', 'M(标准化)']\n",
    "z_cdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 990,
   "id": "d0a2bba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1024x512 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 模型构建\n",
    "SSE = []\n",
    "for k in range(1,9):\n",
    "    estimator = KMeans(n_clusters=k)\n",
    "    estimator.fit(z_cdata)\n",
    "    SSE.append(estimator.inertia_) #样本到最近的聚类中心的距离平方之和\n",
    "X = range(1,9)\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] #设置中文字体\n",
    "plt.figure(dpi=128, figsize=(8, 4))\n",
    "plt.xlabel('k值')\n",
    "plt.ylabel('SSE')\n",
    "plt.plot(X, SSE, 'o-')\n",
    "plt.title('肘部图')\n",
    "plt.savefig('肘部图', dpi=128)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 991,
   "id": "feb9a138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-51 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-51 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-51 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-51 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-51 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-51 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-51 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-51 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-51 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-51 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-51 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-51 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-51 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-51 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-51 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-51 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-51 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-51 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-51 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-51\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(max_iter=100, n_clusters=4, n_init=4, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" checked><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(max_iter=100, n_clusters=4, n_init=4, random_state=0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(max_iter=100, n_clusters=4, n_init=4, random_state=0)"
      ]
     },
     "execution_count": 991,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmodel = KMeans(n_clusters = 4, n_init = 4, max_iter=100,random_state=0)\n",
    "kmodel.fit(z_cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 992,
   "id": "65cf36be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 3, 1, 1, 1, 1, 1, 1, 0, 1, 1, 3, 1, 2, 1, 1, 0, 1, 1,\n",
       "       1, 2, 2, 0, 1, 1, 0, 2, 1, 1, 0, 1, 1, 2, 1, 1, 0, 2, 1, 0, 1, 0,\n",
       "       1, 2, 0, 0, 1, 1, 2, 1, 1, 0, 0, 1, 0, 2, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2,\n",
       "       2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1,\n",
       "       1, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 0,\n",
       "       0, 1, 1, 0, 0, 1, 1, 2, 2, 2, 2, 0, 1, 1, 2, 2, 0, 0, 2, 2, 0, 1,\n",
       "       1, 2, 1, 0, 2, 1, 2, 1, 1, 2, 2, 0, 1, 1, 2, 1, 2, 0, 2, 0, 1, 2,\n",
       "       1, 1, 2, 2, 0, 1, 2, 1, 2, 1, 1, 0, 2, 0, 2, 2, 1, 1, 1, 1, 2, 2,\n",
       "       2, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       1, 0, 0, 0, 2, 2, 2, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2,\n",
       "       1, 1, 1, 1, 2, 1, 1, 0, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1,\n",
       "       0, 0, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 0, 1, 1, 1, 0, 1, 2, 0, 0],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 992,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmodel.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 993,
   "id": "06bcee53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.42969582,  1.3564946 ,  0.80103947],\n",
       "       [ 0.56951791, -0.1788997 , -0.12093169],\n",
       "       [-1.06852006, -0.55629964, -0.40113354],\n",
       "       [ 0.2909904 ,  6.48079154,  8.35458012]])"
      ]
     },
     "execution_count": 993,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看聚类中心\n",
    "kmodel.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 994,
   "id": "a272c17d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    141\n",
       "2     95\n",
       "0     48\n",
       "3      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 994,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(kmodel.labels_).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 995,
   "id": "01593cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>各类别人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429696</td>\n",
       "      <td>1.356495</td>\n",
       "      <td>0.801039</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.569518</td>\n",
       "      <td>-0.178900</td>\n",
       "      <td>-0.120932</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.068520</td>\n",
       "      <td>-0.556300</td>\n",
       "      <td>-0.401134</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.290990</td>\n",
       "      <td>6.480792</td>\n",
       "      <td>8.354580</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R         F         M  各类别人数\n",
       "0  0.429696  1.356495  0.801039     48\n",
       "1  0.569518 -0.178900 -0.120932    141\n",
       "2 -1.068520 -0.556300 -0.401134     95\n",
       "3  0.290990  6.480792  8.354580      2"
      ]
     },
     "execution_count": 995,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计所属各个类别的数据个数\n",
    "r1 = pd.Series(kmodel.labels_).value_counts()\n",
    "# 找出聚类中心\n",
    "r2 = pd.DataFrame(kmodel.cluster_centers_)\n",
    "\n",
    "# 横向连接（0是纵向），得到聚类中心对应的类别下的数目\n",
    "r = pd.concat([r2, r1], axis=1)\n",
    "# 重命名表头\n",
    "r.columns = ['R','F','M'] + ['各类别人数']\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 996,
   "id": "e8211a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>各类别人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.429696</td>\n",
       "      <td>1.356495</td>\n",
       "      <td>0.801039</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.569518</td>\n",
       "      <td>-0.178900</td>\n",
       "      <td>-0.120932</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.068520</td>\n",
       "      <td>-0.556300</td>\n",
       "      <td>-0.401134</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.290990</td>\n",
       "      <td>6.480792</td>\n",
       "      <td>8.354580</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          R         F         M  各类别人数\n",
       "0  0.429696  1.356495  0.801039     48\n",
       "1  0.569518 -0.178900 -0.120932    141\n",
       "2 -1.068520 -0.556300 -0.401134     95\n",
       "3  0.290990  6.480792  8.354580      2"
      ]
     },
     "execution_count": 996,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1 = pd.Series(kmodel.labels_).value_counts() #统计各个类别的数目\n",
    "r2 = pd.DataFrame(kmodel.cluster_centers_)#找出聚类中心\n",
    "\n",
    "r = pd.concat([r2, r1], axis = 1) #横向连接（0是纵向），得到聚类中心对应的类别下的数目\n",
    "r.columns = ['R','F','M'] + ['各类别人数'] #重命名表头\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 997,
   "id": "a9d0dd25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hQ4cCF198caB48eLxKpEJKbiFbq963UIDUSi9/wpjCknhKKApvIX20KlPKDSs6P8P/S38/vvv8e5LAEJayaT/RB4gA/xLhwlYsGBBvMvUK6Hp7er/UB+Q+lgeeeQR12+j2WA6jpJ+6qSFA7VejUc9RerdSKp/QzO6dDwkLRCotVki0cKHejz1xGiasmbm6NAKmsa8ceNGdzyrP//8081O0wyb0zmUhXfYilMtongm1BdSqVIlN21fixOqZ0rT1iP1AyVFs6HUu6M+Fc1e0vuaWu+xesD0vooOvqk+Fr3X3uwprwdIU8rHjRvnPo9wK0FrKr4OhaFt9hYQDEe9Surd0npI6lXyFkFUj456zrTd6kXT31s4yf3s9HeqnhuPPhP18eix9Z7qb0fvU2hflPpu1CemGXLqKVJvkt4b/c14a0HpgKraNi3C6B1mQ8staBZb6PNFmsmnvh+tAu2tEB6JZonpczrVYwLh0AQNRKDFDtXgq8CjHZGOe6QmVzW+ai0WNYSq4Vi/qzE2lHYaWoRPl6tpVLSzUFN0coRrmg63I9cOxzugqQ69oeZWNWdrZ6MGUO3QvOfUooxJ7XTVcBvaLB1pEUVR4+mrr74aXDFY74GmgWs9GK3eG4m2SU2vXiDQlPVwh9BIDu0g1XAbOtU/td5jLRbpLRjprXWj1xm6hpL3Xigcnw3t0NW0rPdYzdahK0Dr81OgUjO0VqSOFBC0XIFCkkKYliFQmNehLzz63vvYY48lCoBay0nvqxYA1d+wFqhM2BSu5Qq8hTF18l6zpt57FBjldA/fAaQlAhCQxLdL7QT0jV0HltTORKFHM8O8tWC049csogoVKrhZQtppaR0bVYVUIdIOxgtAWhNF90/qyN3earyqiGhmS2gFKSFvh6Odlr6Na20czaaJtFCeqhZJLTCnb9GhAUg7QZ0S0owerRasb/n6dq+dpLZF6wEpROmAlqe7eKAqQqGrCCdF2+nNcEsotd7jUN5MOC38F8o74r13uIgzpdCsBQhVUQl37CzNaNNq2fq71KEjFJIS0orWuk4VMr0+HWE9dP0ihTq9j1otPJTClY5fpwqiVp4OXdvHo89e6wHp9eukmXEKh6FVPFWQxJs9lhKSuwgkkGxpNtgGxAg1NA8YMMDNQlIfg3od1BexYsUKd703S0ZNprqNrtPsl6eeesqd1/9W1113XeCrr75K9nOqZ+Jf//qXu6+3Rk/Dhg1dc/PZUMOxN4tLP5Ny//33n3LGlnpX1ISrBlevedXrAdK2apvVB6XXENocG4kaq/WcapRNLq2Nk9wekdR4j72ZWb/88ku8y9WArcu9xmL1YKnZ2puFlZweIM3Y0/X6e/LWEwpH/VLezLg333wz7G1++ukn14Ssv2E1dKv5W7744gvXjKy/0YQN/vrM1FSux77oootcs/OZUBO0HkPN7pF6gOSNN95wjeah2xGuB0hN3a1atUr0PPQA4WwQgIAEvvnmG7djqV+/vtvJezsBzTJSM6xm5WgnpwBUrVq1wLFjxwItWrQI7pD0j/rp7pjV6Kr7e023mv6t802bNk12I7MeR42/r776qmugrVixotsRpUQAUiOtAp6WBNAsqdAFFEOboBUm9Br0OJol9Z///CfJGUtpFYBS6j1W6NFnHG4KvGYH6vEUMCJJKgDp/t4sN01LP5VevXq52+o+miIf7n3W36b+HsqUKeNuW716dbeEg84nDDe6v7d9muWmn1deeWXExSf1/8OXX37pGtBbtmzpwr9oAU49hwJU6DaFC0A33XSTe57QRujQAKRt7NixozuvhRcTIgDhbBCAgDBCV1HWP8La+Wvab65cudyOU/9gewFI9A1WVQXtHHPnzu3WkknOTlX/2GvqsP6B1xo43iwo7Tg0RVyXa20afeMPt6q0woDCg6Ye58uXLzgTSTtF7TC08zubAKSKgAJdiRIl3HUKhQkDUrhZYJpSrZ2g7qPZcQqOmlEXjQB0tu9xqH//+9/u9loROSHNPNN13pIIyQ1AChJaSdr77DT7KrnVPW9WmE76DCJV77RN3krQXnVHM928aqaCkrdt+ptW9apevXrBaqbWVfK+HCh4XX311fFmPCoYexWa9u3bu8u0nlaocAFIn4cqrKFBSSFe99fyCJrRp99VuQu3NhMBCGeDAASEoenC7733nqvs6B96LSingKAF5DyhASh0zZxatWoFdwoKJvo2r+EIj3aymlJ/zz33uNvotp06dQo7BVwBxgsSmsasoTmt46IdlldxUeDSTqRmzZqBvn37uurUnj17znghQ28nquGYYsWKuctU9dEQz+lMg9e6NdqpKhR6j51wp+gFoDM5JRWAUvI9TrhApqooCYOSgkTp0qXdcymYRKIQoMf44Ycf3Pmvv/46UL58+WBoVdA+HXoub7hJJ/2t6vV44XXevHlunSoNqWltIy2XoCUctDinbq/lABTmFWx1/rLLLgtWhvbt2xeoUqVK8PPX1HstA+EN0WmobODAge45VCHU83Xv3j34HiV8/5o1a+a2zxsaVehRENMU/XDvs07629I6WpEQgHA2CEBAAi+//HLw+FraATz33HNhFxcMF4BCexb0D76381Wfhr7ZqxKhITTvH3iFpaR6PUTPrfWGvJ20F640DKMqk0LEgQMHIt7fC0Bak0WVlkgnb2E6LwB9/vnnbueixQFVdTnTdYA0bKQdpYYyEg7TeAFIKwIntW2hJ+/4YgkDkHbYqfUea80nb2hI6wdpLSFvdXAd28obYgpdNFCvTX8DCl4KHl4VSmFagUEGDRrkLtOOXCsynykvxClMKMBo5WvvtehvWWtVaaXw0MCmYTO9L6ps6naqbiUcFtNq2xre8ioy+mKglcL1XofS4+j/Bd1OQ68J1/YRBUtdr/dBn6HeG51XQAvlVaLUn6XqXVIIQDgbBCAgzA5bK+aearG6pAKQRzsMbzVhVYP0zVnfjhUIkns4BI96MXSIAy1Cp8pMcodKzmYIzNtRp+RCiKk5BJYa77HCgnbqOXPmdNUOb0FGVYtUGVGg0W07d+4cL4jq7yhh1UoVkNCDgHoVj5RYvXvHjh3BYSId3kPDS3rs0KplQl7fz5AhQ5Ls1VIjtw79Eo5es4bXVMFSyI0UxlWVVMjyDuyqz0mh21s5O/Tz1RDqqVa7FgIQzgYLIQJpRAvoaSHFs10nxsO04LR7j7UYoQ7qGW5afFK0oKEeQ8sjaC0pTQvX4oMZjRZo1NT4031/gGgiAAEAAN85/fXnAQAAYhwBCAAA+A6HwohAY/a//vqrW0aePgsAAGKDOnt0qB4dRy+pAy0TgCJQ+NERiQEAQOzZtm1bvGPgJUQAisA7QKLewLx580Z7cwAAQDLs37/fFTCSOtCxEIAi8Ia9FH4IQAAAxJZTta/QBA0AAHyHAAQAAHyHAAQAAHyHHiAAQIZ24sQJO3bsWLQ3AynknHPOsSxZspz14xCAAAAZdj2Y3377zfbu3RvtTUEKy5cvnxUuXPis1ukjAAEAMiQv/Jx//vmWK1cuFrXNIKH2n3/+sd9//92dL1KkyBk/FgEIAJAhh7288FOgQIFobw5SUM6cOd1PhSB9vmc6HEYTNAAgw/F6flT5QcaT6/9/rmfT20UAAgBkWAx7ZUyZUuBzJQABAICoO3z4sDsQeVohAAEAkA79+eefNmfOnGTf/vPPP493/pZbbrG33noreP6PP/6wb7755oy25YknnrCOHTsmeRsde2vRokURr1dP1tatW93vZcuWtZ9++skFno0bN7rLnnzySevRo4elFQIQAADp0MCBA+3ll19OdvWke/fudvvtt9vx48cTXa+G4fr169uoUaPiBZJMmTJZ1qxZ4510mWbQnQ49/4EDB5I8+vqbb75pnTt3jnfZ6NGjrVu3bu73lStXWrVq1SytMAsMAOArJfvMStPn2zKsyWnfZ/bs2S78eAElIVVOypUrZ99//707nyNHDld9adiwod133302YcKEeOHnmmuucafx48cneqzjCQKTHksWLFhgXbp0CT5HUrzKTlLT0u+55x4bPny4LVu2LFiRGjx4sH3xxRduart+Pvfcc5ZW0n0FaN26dS4RLl68OOz1P//8s2XLls3uvvvueJfrD0H30wdZsWLF0yojAgAQLatWrbJ27dpZhw4drEaNGi4oKKR4pylTplihQoVs2LBh8e6nKeGfffaZCxqhdNtBgwbZpEmTwoaplLB27Vr387vvvkty5pb2zVdddZU7X7BgQfvggw+sfPny9vHHH7sqUu/eva1Ro0bB0/bt2813FSD9ASgp6k05dOhQxNs99thjiabBbd682Ro3buzKam+88YaNHTvWWrRoYRs2bLCLLrooDbYeAIDTt2LFCrf/euWVV6xVq1b2zDPP2LXXXmvjxo2zEiVKWN++fW316tVu3+gFiVdffdUeffTReI+zf/9+t2/Ul/8HHnjAXdazZ89EFRuvVyjUmUwtV7DJnDmz265Iw1gaHlOAO3r0qP31119Ws2ZNt4ZP7dq1befOne5nmzZtgv1Aev3qK/JdAJoxY4ar7MycOdMaNGgQ9jZz5861r7/+2q688spEY4pqsFKAEo15fvTRR/baa6+5NxXwe0k+vTiToQEgI9OIxSeffGLVq1d35x9++GE33KVwoH3i1Vdf7YaKQhd3vOuuu9xJ9EV/yJAhLmAoaChgKHi0bNnSDY9lz549eL8DBw64ClO9evVcP5BXIFD/j57rdMyaNcuFsMmTJ9vjjz8edpq6RmzUlK0KlkKc9u0Ke6oENW/e3FV8vNEcBb17773X4uLizHdDYBoX1BtZunTpsNcfPHjQjU2OHDky0UJX8+fPd2+kRyU//fEsXbo04vMdOXLEJebQEwAAab3KsQLB+++/b7169XKhRENXL7zwgqv0aJhIfTbap3Xt2tVViNQrpC/81113nSsIVK5cOVgh0pCSRkOmT5/ugpCG1lQQOHr0qOXOndvtF1VlWb58uWug1kktJ/nz5z+t2We7d++2/v37W968ed3jJ6Q+JA11/fjjj257b7rpJrftmumm/p958+YFZ6ht27bNXVamTBlLTVljdZGj+++/372ZSrWhXe2yadMmK1WqVLzLVDr0xijDGTp0KNUhAEBUaZ+mkQ8NI11//fX24Ycf2hVXXBG8XgFGbR5qklYlSG0e3kFf27dvb1OnTrVixYq522o/qLB0+eWXuxlYv/76qwtKGmZavHixazVR5eaXX35xAUWhxDNixIhkb7N6kfSYKkao/0gVoCZNmsTrN1J/knp6zz33XBfQnnrqKbdf9obnREFMlSENA6phW0d9T03ptgKUlGnTprmEOXHixLDXq6yXsCqk80rOkajctm/fvuBJCRQAgLSk/ZpGICpUqOBCgio6CaepX3zxxW69HFWL1HSsIKMApGKAF368dhANhXmKFi1qL774ogs81apVc8FIoUcjLZdddpn7XaevvvrKjbIkd3+8ZMmSYAFBAUh9u6ruhNI+VRWdCy64wBUjNGSno7nrpOqUF/40qqO+Xa8XKDXFXABas2aNe4Pffvtt98aFozFOlfdCKfwkdUwY3Uelu9ATAABpSUNPmr2sg7mqChM6+yv0pOt0G68Co54gDWl5J+3vVHFRn03o5TqpMrRx40bX/6PFDTVLTG0j+l0n7Q9De4Ui0fR4revTp0+fYPBS1eb55593axipuuQpXry4G51RT48C1q5du+yHH35wvUZeH6/WMXr33XfddW3btrXUFnMBSOOgqvDceOON7o/EW/tAZUD9rs52fRAJKzg6H6mfCACAWKXqi/aLoScNLdWpU8dVXDQTLOH15cqVc/fV8Jmu95quRf03GqpSGFNfUThqttYQnRqo+/XrF+86NTZrVEUVndCVqDUEp+0ZMGCAe85bb73VOnXqFGxZUR+RApRC0tkc5DTDBqCnn37alfzULOWd9MFpmrt+V4mvVq1aboaYRylZCzpFmk0GAEBG8dJLL7kFD19//XW3IKL2j6FBJOGM6xMnTrip9qLf1QaiEKIhstAFFT3vvPOO69HRkJZ+1/T3cBOZVE1SX5Km2WstI83OVt+SgpkqTpqwpKE+zXLTYTF0mYbG1ASuIofu48sm6EjU0JVwpUmV+vRhXXrppcG1DtT9roWflEDVca43+FTHMQEAZHyxsvyCpsBreCkcBRU1RHsUFrQIosKPhpAWLlzoKis6qWdWt9UIyp133umqOuXLl3e9QJohptWX1V6iAoJmdKlhWW0g3sFJ1XekAoP2vRp+0z5Vs7A13V7T7CPR2kVa60cN25rYpG1777333OKG//nPf9xjq6ihwPXII4+4sKRtVN+SwlClSpXsyy+/TLXRm5irACWHOua1zoCmDqo6tH79eveHkZoLKgEAkJKeffZZF0LCnXSdR9PlFV7UPKwKioantJ6QR2sHqd9GjdMKHioQzJs3z1q3bm3NmjVz4ejmm2920+Q1w1qNyKIQpaEw9QNpbSI1SCsMaURFM6eTCj8eFR40M0231SEwHnroIbd9WvdHlSlNv9d16mlS07acd955rrVFAbBkyZKWWjIFNACIRNSFr6qSkikN0UhtLIQIpCyFBE0XVwXEO7ZVRqX9lIKJwkpyaJJQtmzZgkvGRFp2RrfbsmWL6xNSVUjBJBY+3+Tuv2NuCAwAAPzP6a6W7K3yXPoUQ0u6ndcsnRFlyCEwAACApBCAAACA7xCAAAAZFm2uGVMgBT5XAhAAIMPxjiOlRf2Q8fzz/z/XszleGE3QAIAMR1Or8+XL5w7y6a0Xd6qDbCM2Kj8KP/pc9fkmZyp+JAQgAECG5B0v0gtByDjy5csX8XigyUUAAgBkSKr4aPVirWGTFseWQtrQsNfZVH48BCAAQIamnWVK7DCRsdAEDQAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfCfdB6B169ZZtWrVbPHixcHL9uzZY506dbICBQpYXFyc/etf/7K1a9fGu9+iRYvc/XLkyGEVK1a0OXPmRGHrAQBAepRuA9CqVausTZs2VqNGDfd7qMGDB1uWLFls1qxZNnv2bHdZo0aNbP/+/e73zZs3W+PGja1hw4a2fPlyq1OnjrVo0cK2bt0aldcCAADSl3QbgGbMmGHZsmWzmTNnJrqud+/eNnHiRLv66qutZs2a9uabb9rOnTttyZIl7vrRo0db2bJlbfjw4VapUiUbNWqU5c+f31577bUovBIAAJDeZLV0SlWeTJky2ZYtWxJdV6JEiXjnNcwlJ06ccD/nz5/vKkKerFmzWu3atW3p0qURn+/IkSPu5PGqSQAAIONJtxUghZ/kmjBhguXMmdNVhGTTpk1WqlSpRKFpx44dER9j6NChrp/IOxUvXvwsth4AAKRn6TYAJZeGwvr162cjRoxww1xy4MABy5UrV7zb6fzhw4cjPk7fvn1t3759wdO2bdtSfdsBAEB0pNshsFNRmOnZs6fr/xk3bpzdddddweuyZ89uR48eTXT7hKEolO6jEwAAyPhiMgAdOnTIbrjhBtf4rL6eK664It71xYoVS1TB0fnSpUun8ZYCAID0KCaHwAYMGGC7d++2ZcuWJQo/UqtWLZs7d27wvJqjFyxYYA0aNEjjLQUAAOlRTFaApkyZYm3btnULIurkyZ07txUuXNgNjV111VU2aNAga9mypb388st28uRJ69ixY1S3GwAApA8xWQHS0Jeani+++OJ4p+7du7vrVRVSSJo0aZJVr17d1q9fb5999pnlyZMn2psOAADSgXRfASpZsqQFAoF4l6macyq33HKLOwEAAGSIChAAAMDZIAABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfIQABAADfSfcBaN26dVatWjVbvHhxvMvff/99K1++vOXIkcOuvPJKW7lyZbzrFy1a5O6n6ytWrGhz5sxJ4y0HAADpVboNQKtWrbI2bdpYjRo13O+hlixZYrfeeqt17tzZvv76aytevLg1btzYDhw44K7fvHmzO9+wYUNbvny51alTx1q0aGFbt26N0qsBAADpSboNQDNmzLBs2bLZzJkzE1337LPPWpMmTeyBBx6wypUr22uvvWb79u2zd999110/evRoK1u2rA0fPtwqVapko0aNsvz587vbAQAApNsANHjwYJs8ebKVLl060XXz58+3Ro0aBc/ny5fPqlatakuXLg17fdasWa127drB68M5cuSI7d+/P94JAABkTOk2AGXKlCns5X/99Zft3bvXSpUqFe/yEiVK2I4dO9zvmzZtSvL6cIYOHWpxcXHBk4bVAABAxpRuA1AkXp9Prly54l2u84cPHw7eJqnrw+nbt68bRvNO27ZtS5XtBwAA0ZfVYkz27Nndz6NHj8a7XOHGCz26TVLXR3pc77EBAEDGFnMVoIIFC7qgkrBCo/Nev1CxYsWSvB4AAPhbzAWgzJkzW82aNW3u3LnByzRkpXWAGjRo4M7XqlUr3vUnTpywBQsWBK8HAAD+FnMBSHr16mXTpk2zcePG2Zo1a+zOO++0cuXKubV/pGfPnrZs2TIbNGiQrV+/3nr06GEnT560jh07RnvTAQBAOhCTAahZs2b24osvuoCjapCmsGu9oCxZsrjrr7jiCpsyZYpNmjTJqlev7kLQZ599Znny5In2pgMAgHQgUyAQCER7I9IjrQOk6fAaXsubN2+0NwcZXMk+s8yPtgxrEu1NAODT/XdMVoAAAADOBgEIAAD4DgEIAAD4DgEIAAD4DgEIAAD4TswdCgMAYh2z/oDoowIEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8J2YD0N9//2333HOP5c+f3/LkyWOtW7e2nTt3Bq8fO3aslSpVynLmzGn169e3TZs2RXV7AQBA+hGzAahHjx62aNEie++992zOnDm2ZcsWu+2229x106dPt169etmgQYNs8eLFduzYMWvevLmdPHky2psNAADSgawWo5YvX27du3d31R3p379/MAANGzbMunTpYu3bt3fnx48fbxUqVLCFCxdavXr1orrdAAAgRitAr7/+uj3//POndZ8vv/zSBZL9+/dbSmjTpo1NmzbNdu3aZXv37rXXXnvNXabfV69ebY0aNQretnz58lakSBFbunRpijw3AADwYQVo1apVbmjpggsusIsuusjKli3rfo/k4MGDdscdd9i2bdtc1aZGjRp2th577DGbN2+eFS5c2DJlymSXXnqpff311/bTTz+569X/E6pEiRK2Y8eOiI935MgRd/KkVFADAAAZqAdozZo1dvvtt1vt2rWtaNGiLmC0atXKJk2aZPv27Qve7vjx4+52akIePHhwioQfue+++1z159NPP3VBKFeuXK4CdODAAXe9zofS+cOHD0d8vKFDh1pcXFzwVLx48RTZTgAAkIF6gJo1a2bDhw93VZUNGza4Yaf58+fb+++/b9myZbN27dpZz549rU+fPq5J+d5777VHHnkkRTZazzdx4kRbtmyZXXnlle4yPa+qPnXr1nXnjx49Gu8+Cj8JQ1Govn37Wu/eveNVgAhBAABkTGccgBRyLrnkEnfyGpFl/fr19sorr7ienDfeeCMYLp566qmU2WIzW7t2rftZpUqV4GUaiitYsKCb8SUabitTpkzwep3XVPlIsmfP7k4AACDjS9YQWCAQsE6dOtnbb79tv//+e8TbKXxoOvqPP/7oKi7nnHOOu29S9zkTxYoVcz+/++674GW//fab/fHHHy70lCxZ0ubOnRu8buPGjbZ9+3Zr0KBBim4HAADIwBUgBYvPPvvM3nzzTddwrKEkVV80BKXKyvfff+9mWH3xxRdu6Ojiiy+2ESNGuIUKVf15+eWX3ZCUKkK6/9m65pprrGrVqtaxY0cbOXKkq0apKVp9SDfeeKPt3r3bPa+2UcNiWhOoadOmVqlSpbN+bgAA4JMAVKhQIdfro7Cjfp4ZM2a4CkvNmjXd9WqCViB59NFH7frrr7dq1aoF7ztmzBg3tPTCCy+4MPLEE0+c9UZnzpzZNT8r2LRs2dJOnDjhen/++9//2rnnnutmmikEde3a1VWitAiitgMAAEAyBTRGdQY2b97sqjwTJkxwU+AViryG5HBuvvlm++ijj2z27NkxMRSlSpZmg2lGW968eaO9OcjgSvaZZX60ZVgT8yM+byD6++9kT4NXBUhNxN4083Hjxlnbtm3d7K9bbrkl3qyrX375JdEsLC2eqPWCkpqKDgAAkBaSHYDUZKxjb6n/ZuvWrfbhhx/azz//bBUrVnTndRgKbwaWmpDVjKyZYKLQo6qPeoGaNOEbAAAAiJFp8OrrWbFihRvKUh+QZ9SoUa4fR/03mvXlqVWrlnXu3NkWLFhg1atXd6tHn3feeSn/CgAAAFJzJegLL7zQHdNLixp6qzyruViVHa8h2vPkk0+68PPxxx+7ZmUNn4Wu2wMAAJDuA5CqO5purinw+qm1dVTh0SEuFIh0mWZehbr22mttyJAhbi2gyy+/PDW2HwAAIPWGwHT0d28NHwUareysBQ7V2KxDXuiyAQMGuDWDQtf6mTp1qluhWcfauvvuu92UegAAgJioAGltnW7durlDX+hnvnz57MEHH7SdO3e6KWe6Xg3RWhxRJk+ebF999ZVbIHHWrFmugvTcc8+l5msBAABI2QCkCo9WV77hhhvcmj9y2WWXuentqvxMmTLFevTo4YbEdFsdKFUzv7RQodYH0qKEmhXmzRQDAABI9wFIM8BefPFFGz16tJsJ5tHvHTp0cAFHK0VnzZrVDYGp+Vk9QP3793e3UxO0d0gNAACAmAhAquKouqOgI+XLl7f8+fO735955hkXfN599113XhUgHfbi888/Dx5/S0drL168uH377bep80oAAABSuglaChcuHPzdGwYTNTnryOxeg7OGubJkyRK8/siRI+5YYV26dLFHHnnkdJ4SAAAguusAJSV0dldo+PEqRKoe6WjyZ3joMQAAgPQXgCJZsmSJDR482A2RjR8/Pt4UeQAAgHQ/BHa6NmzYYM2aNbMTJ064KlDt2rVT8+kAAACiWwHSITPq1atne/bssccee8ytGQQAAJAhA5CqPVr1WWsA/fXXX+44YRoCAwAAyJBDYB999JFbFHHdunVWuXJlt0giB0AFAAAZrgK0d+9ee+mll1zguemmm2zfvn02YcIEW7lyJeEHAADEdgVI1Ryt7nzhhRdasWLF3GyuOXPm2OLFi931GvIaOHCgtWjRwjJnTvXJZQAAAKkfgP7880/75JNP7OjRo4mua9iwoXXq1MmaNm1K+AEAAOlestOKZnEdPnzYNTarx+ett96yhx9+2KpVq2bz5s2zW2+91VWG+vTp4475BQAAkF6ddrkmLi7OKlasaG3btnVHfF++fLk7COqIESMsT548br2fMmXKuJ8nT55Mna0GAAA4CykyXlW0aFHr1auX/fzzz+5wFzlz5rS+ffvaNddcYzt27EiJpwAAAEgxKdqwo/6f9u3b2w8//OAqRMuWLXNHkV+zZk1KPg0AAMBZSZWOZQ2TTZ482YYMGWK7du1yTdLffvttajwVAADAaUvVKVs6BIZ6g3Q4DB0TTDPJAAAAoi3V56yrN6hbt262efNm69y5c2o/HQAAwCmlyaI9zz33nJUvX95mz55t27dvT4unBAAASJtjgUWSLVs2NztMq0drJWkAAIAMH4CkevXqafVUAAAASeK4FQAAwHcIQAAAwHcIQAAAwHcIQAAAwHcIQAAAwHcIQAAAwHcIQAAAwHcIQAAAwHdiPgDt3r3bOnbsaAUKFLCcOXPaTTfdFLxu7NixVqpUKXd5/fr1bdOmTVHdVgAAkD7EdAD6+++/rXbt2rZz50774IMPbOnSpdauXTt33fTp092BWAcNGmSLFy+2Y8eOWfPmze3kyZPR3mwAAOCXQ2GkhmHDhlkgELCPP/7YHW9MKleuHLyuS5cu1r59e3d+/PjxVqFCBVu4cKHVq1cvqtsNAACiK6YrQG+88Yb17NkzGH48e/futdWrV1ujRo2Cl+lo9EWKFHFVIgAA4G8xG4B++eUX+/XXXy1PnjxWt25d1wN03XXX2YoVK2zz5s3uNur/CVWiRAnbsWNH2Mc7cuSI7d+/P94JAABkTDEbgNT3IyNGjLBu3brZzJkzLV++fHbDDTcEw0uuXLni3UfnDx8+HPbxhg4danFxccFT8eLF0+BVAACAaIjZAHT8+HH388EHH7RWrVpZzZo1bdKkSS78qM9Hjh49Gu8+Cj8JQ5Gnb9++tm/fvuBp27ZtafAqAABANMRsADr//PPdz7JlywYvO++889zlaoyWhCFG50uXLh328bJnz2558+aNdwIAABlTzAagMmXKuLAT2tT8xx9/2O+//25VqlSxkiVL2ty5c4PXbdy40bZv324NGjSI0hYDAID0ImanwWfOnNl69+5tgwcPtqJFi7rKTv/+/a1cuXLWuHFj1yStYS2FITVDa02gpk2bWqVKlaK96QAAIMpiNgDJI488Yv/884/df//9rvdH6/uoGfqcc86x7t27u1Wiu3bt6np/tAjimDFjor3JAAAgHcgU8BpmEI8ClWaDqSGafiCktpJ9ZpkfbRnWxPyIzxuI/v47ZnuAAAAAzhQBCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+A4BCAAA+E7WaG8AwivZZ5b50ZZhTaK9CQAAH6ACBAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfIcABAAAfCdDBKDJkydbpkyZ7K233gpeNnbsWCtVqpTlzJnT6tevb5s2bYrqNgIAgPQj5gPQ4cOHbcCAAfEumz59uvXq1csGDRpkixcvtmPHjlnz5s3t5MmTUdtOAACQfsR8ABo8eLBdeeWV8S4bNmyYdenSxdq3b2/VqlWz8ePH2/r1623hwoVR204AAJB+xHQAWrt2rb388ss2cuTI4GV79+611atXW6NGjYKXlS9f3ooUKWJLly6N0pYCAID0JKvF8NBXu3btrF+/fla8ePHg5Zs3b3Y/1f8TqkSJErZjx46Ij3fkyBF38uzfvz9VthsAAERfzFaA1ONToEAB6927d7zLDxw44H7mypUr3uU6r9AUydChQy0uLi54Cg1VAAAgY4nJADRx4kSbMWOGTZkyxTJnjv8SsmfP7n4ePXo03uUKPwlDUai+ffvavn37gqdt27al0tYDAIBoi8khsKeeesr27NmTaJjrzjvvtIsuusj9rgBTpkyZ4HU637p164iPqeDkhScAAJCxxWQA+vzzz93U9lBqdFYwatmypTVs2NDmzp1rdevWdddt3LjRtm/fbg0aNIjSFgMAgPQkJgNQaGUnlGZ66Tr1BWlIq0qVKq5KpH6hpk2bWqVKldJ8WwEAQPoTkwHoVLp37267d++2rl27ut4fLYI4ZsyYaG8WAABIJzJMAAoEAsHfdVgMrQKtEwAAQIaYBQYAAHA2CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3CEAAAMB3YjoArVmzxq6//nrLlSuXFS5c2Dp16mR79uwJXj927FgrVaqU5cyZ0+rXr2+bNm2K6vYCAID0IaYD0H333Wd169a1pUuX2quvvmoLFy60Dh06uOumT59uvXr1skGDBtnixYvt2LFj1rx5czt58mS0NxsAAERZVothU6dOtRIlSrjfL7/8ctu3b5+1b9/e/vnnHxs2bJh16dLFnZfx48dbhQoVXEiqV69elLccAABEU0xXgLzw48mRI4er8Ozdu9dWr15tjRo1Cl5Xvnx5K1KkiKsWAQAAf4vpClCoQCDghsFq1Khhu3btcpep/ydhYNqxY0fY+x85csSdPPv370/lLQYAANES0xUgj/p77r33Xps/f76NGTPGDhw44C5Xc3QonT98+HDYxxg6dKjFxcUFT8WLF0+TbQcAAGkv5gPQ9u3bXSP0rFmzbMGCBVa9enXLnj27u+7o0aPxbqvwkzAUefr27et6iLzTtm3b0mT7AQBA2ovpALRx40Y35JU3b143Jf6qq65ylxcrVsz9TBhidL506dJhH0uhSY8TegIAABlTTAegtm3bWp06dVz1p1ChQsHLFYBKlixpc+fOjReWVC1q0KBBlLYWAACkFzHbBP3jjz/aypUrrU+fPokWOFQY6t27txvWqlKlimuG1ppATZs2tUqVKkVtmwEAQPoQswFo586d7merVq0SXTd69Gjr3r277d6927p27ep6f7QIohqkAQAAYjYA1a5d2019T4pWgdYJAAAgw/QAAQAAnAkCEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8B0CEAAA8J2sloEFAgEbNGiQjRs3zvbt22c33HCDjR8/3goWLBjtTQMA+ETJPrPMj7YMa2LpWYauAD377LM2atQoF4Dmzp1rGzZssDvuuCPamwUAAKIsw1aATp486QLQgAED7MYbb3SXjRw50ho3bmybN2+2UqVKRXsTAQBAlGTYCtC6devsjz/+sEaNGgUvq1u3rmXOnNmWLl0a1W0DAADRlWErQJs2bXI/Qys9OXPmtEKFCtmOHTsS3f7IkSPu5FHPkOzfv9+i4eSRf8yPovV+Rxuft7/wefsLn3d0nld9wL4MQAcOHHDVnuzZs8e7PFeuXHb48OFEtx86dKg9+eSTiS4vXrx4qm4n4ot7IdpbgLTE5+0vfN7+Ehflz/vvv/+2uLg4/wUgBR/1AR0/ftyyZv3fy1T4UQhKqG/fvta7d+/ged33zz//tAIFClimTJnML5ScFfq2bdtmefPmjfbmIJXxefsLn7e/+PXzDgQCLvwULVo0ydtl2ABUrFgx93P79u1WsmRJ97uGuHbv3m2lS5cOG5gSVovy5ctnfqX/Wfz0P4zf8Xn7C5+3v/jx845LovKT4Zugq1at6np+NP3ds3DhQvezdu3aUdwyAAAQbRm2AqTwc99999nAgQOtRIkSljt3bnvggQesS5culj9//mhvHgAAiKIMG4Dk6aeftkOHDlnr1q0tS5Ysdvvtt7u1gRCZhgEff/zxRMOByJj4vP2Fz9tf+LyTlilwqnliAAAAGUyG7QECAACIhAAEAAB8hwAEAAB8hwAEAAB8hwAER73wOhSIVs4899xzrWXLlu5gssjYdNDgatWq2eLFi6O9KUhFa9asseuvv96tgl+4cGHr1KmT7dmzJ9qbhVQ0bdo0q1y5svvMtRTMkCFDTnlsLL8hAMHR8gCjRo2ycePGucUjN2zYYHfccUe0NwupZNWqVdamTRurUaOG+x0Zm9ZEq1u3ri1dutReffVVtyhshw4dor1ZSEXff/+9O8STPvN+/frZE0884f59x/8wDR7uuGcXXHCB+59Ei0XKp59+ao0bN7ZNmzZZqVKlor2JSGH9+/e3rVu3ukpAgwYN7IsvvrBatWpFe7OQSn755RdXBfC888471r59e3e8pHDHRkTG06RJE7ce0IwZM6K9KelGhl4IEckfBtFwV6NGjYKX6dti5syZ3bcHAlDGM3jwYHeQ3y1btkR7U5AGQsOP5MiRw33xgX/o89bBvfE/BCC4Ko+EBh0dSqRQoUK2Y8eOKG4ZUovCD/xJRX8Ng2n4k+pPxnfw4EGbOnWq+zLrHQ8T/4cABDtw4ICr9iRcLl3/OB4+fDhq2wUgZR07dsy6du1q8+fPt0WLFkV7c5DKVOk7cuSI5cmTx8aOHWuXX355tDcpXSEAwQUflUePHz9uWbP+709C4YdviEDGsH37dtf4vnnzZluwYIFVr1492puEVPbNN9/Yvn37bMWKFXb//ffbt99+646Rif9DAIIVK1Ys+A9kyZIl3e/61rB7924rXbp0lLcOwNnauHGj1atXz1UANCVew9vI+C699FL30xvuvOeee2zAgAGuxQFMg4eZVa1a1f0PoenvHm+suHbt2lHcMgApoW3btlanTh2bNWsW4cenVN1X/9eJEyeivSnpBhUguPCjdUIGDhzoZovkzp3bTYfv0qWL5c+fP9qbB+As/Pjjj7Zy5Urr06dPcMKDR2EoLi4uatuG1LF//37r3r273X777VakSBFX9XvkkUfstttuc/++4/8QgOBoXPjQoUPWunVry5Ili/sfR4sjAohtO3fudD9btWqV6LrRo0e7HSUyXvOzGt612KV6gC666CLr0aOHPfjgg9HetHSFhRABAIDv0AMEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEAAB8hwAEICYtX77cDhw4kOjyCy+80B566KFEl69evdr27t2bYs+v4yqNGjUqxR4PQNriWGAAYs7BgwftpptusnPPPdemTJli1apVS/L2b7/9tt19993WrFkzmzZtWvDydu3a2Y4dOyLer2zZsjZx4sSw13388ceWPXv2s3gVAKKJChCAmKPgs3jxYsubN69de+219sYbb4S93dGjR61nz57u4L7NmzdPFGa+/vpr27Vrl1122WWJTtu3b7cVK1YEb9uwYUN7+OGHU/21AUgbVIAAxKRSpUrZF1984Yai5syZYx07dkx0myVLltjkyZPtlVdesc6dO4d9nBo1atiYMWMSXX7LLbfYTz/9FDz//fffW8GCBVP4VQCIFgIQgJiiIa8TJ04Ez7do0cICgYC99dZb7vw///xjGzZsCJ4fPny45cqVK3heKlSoYFWrVo3C1gNILwhAAGLKgAED7PDhw4ku12V79uxxvy9atMjWrFkT8TG6dOkSDEBbt2619957L9FtkuoNAhD7CEAAYkrosJRHgeeOO+5wfToLFiywq6++2jUw33XXXVa+fPkkH0+31ymcypUrp9h2A0hfaIIGELM2b97sZnLVq1fP9exoeCxr1qx26NAhNwymZmavSVpDYwlVr17dBg8ebGvXrrUJEya42xw7dsydHnvsMbviiiui8roApD4CEICYov6fuXPn2s0332wXX3yxzZs3z01Jf/bZZy1TpkzuNpUqVbJZs2bZDz/8YFWqVLFu3bpZ0aJF3YwwBSPP1KlTrX///vbZZ5/ZPffcY5kzZ3YBSqennnrKXn/99Si+UgCpiSEwADFj/vz51qpVK9frkyNHDnvwwQetb9++li9fvuBtPvjgAytUqJD7XcNgL730kg0aNMheeOEFGz16tDtpPaCnn37aVZBEFSBRsAqlQNS4ceM0fY0A0kamgKZPAEAM0NCUZn1pWOvOO++0Cy64IN71x48ft7i4ODd81a9fv0T310rQqhTpn739+/e7cJSUnDlzBofOtMJ0rVq1XNVIcufO7YbdIq1BBCB9owIEIKaMGDHC/fzrr7/cKZQWL1Rg0RCW1u0Jp3379i68nH/++TZs2DB3WadOndzttW6QZ+DAgQyBARkYAQhAzNDU9FPN6pI+ffq4UyRNmjSxmTNnWrZs2dz5TZs2ucdVMArtNdIwG4CMiSZoADGjZMmSbvgq3OnIkSOu56dcuXJ2zjnnuCGwSLdV+PEo/HzzzTduRehQf//9tzvkBoCMiQoQgJinUNO1a1cXZhYuXOgCTY8ePVxF59FHHw3ODos0pJYlSxY3NBZKjdZ58uSJN7wGIOMgAAGIaTt37nQrO3/00Uc2cuRI16is02+//eaaoXWcMB0L7JJLLkl03w8//NDGjh3r7q+eoFDqCbr00ksjBi41ZCcVrACkbwQgADFH4eOrr76yd955xyZNmuR6dd5++21r27Zt8DZDhgxxw1p333236++pU6eOdejQwWrWrOnWD5oxY4ZbPVpDZqoC9e7d24UgVY3UDL1x40a3bpDXDzRq1Cg3w0zP9eWXX7ojzWttIQCxiWnwAGJqHSAdC2z16tVutpcCiw53ofWAChQoEPY+WhX6tddec+sAeYfR0LpB6hnS1PbZs2db6dKlrUGDBrZixQr3uOedd57deuutrqKkGWWisKNqk6dEiRJuuE19SQBiDwEIQMxQmNH6P6raKLBcc801wYCSHKrqaLFDBSc1TCvERApOCWlV6YMHD9rJkyddc3SZMmWCs8gAxB4CEAAA8B2mwQMAAN8hAAEAAN8hAAEAAN8hAAEAAN8hAAEAAN8hAAEAAN8hAAEAAN8hAAEAAN8hAAEAAN8hAAEAAPOb/weAnWklMaZWvAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot(kind='bar',y='各类别人数',rot=0)\n",
    "plt.xlabel('类别', fontsize=14)\n",
    "plt.ylabel('人数', fontsize=14)\n",
    "plt.title('聚类结果各类别人数统计', fontsize=16)\n",
    "plt.savefig('聚类结果各类别人数统计', dpi=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 998,
   "id": "78aa2ea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R(标准化)</th>\n",
       "      <th>F(标准化)</th>\n",
       "      <th>M(标准化)</th>\n",
       "      <th>类别</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>用户id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gx4sJzcQog01UhZL</th>\n",
       "      <td>-0.023908</td>\n",
       "      <td>0.173965</td>\n",
       "      <td>1.447321</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kEXrhTiug93DIcLG</th>\n",
       "      <td>-0.023908</td>\n",
       "      <td>0.874723</td>\n",
       "      <td>1.447027</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AouXr0EOUtSRdiYK</th>\n",
       "      <td>0.605889</td>\n",
       "      <td>1.575482</td>\n",
       "      <td>-0.392408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yds7U30hnRZDiLtb</th>\n",
       "      <td>0.875802</td>\n",
       "      <td>-0.526794</td>\n",
       "      <td>-0.024521</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFDTSXrhN9Q2mbVw</th>\n",
       "      <td>-0.113879</td>\n",
       "      <td>7.181550</td>\n",
       "      <td>6.597663</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4qHSn3dkPzJTAjoG</th>\n",
       "      <td>0.335976</td>\n",
       "      <td>-0.526794</td>\n",
       "      <td>-0.539562</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tXkjbzpTsZcxYPKG</th>\n",
       "      <td>1.325658</td>\n",
       "      <td>-0.526794</td>\n",
       "      <td>-0.392408</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ro43b68MustgPyOR</th>\n",
       "      <td>-0.203851</td>\n",
       "      <td>0.173965</td>\n",
       "      <td>0.711253</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18e2VC0IJ7SkcKzF</th>\n",
       "      <td>1.235687</td>\n",
       "      <td>0.173965</td>\n",
       "      <td>0.711253</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YCEo95zSZ08IJ3PW</th>\n",
       "      <td>0.066063</td>\n",
       "      <td>0.173965</td>\n",
       "      <td>0.122634</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>cfGAgMWTJLt09VDs</th>\n",
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       "      <td>-0.017163</td>\n",
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       "    <tr>\n",
       "      <th>km0xTQGpIYZuXWyL</th>\n",
       "      <td>0.246005</td>\n",
       "      <td>0.874723</td>\n",
       "      <td>0.711253</td>\n",
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       "    <tr>\n",
       "      <th>d9L37fAwopQkWPYh</th>\n",
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       "      <td>-0.392408</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>UXewZPdJbBiycY8Q</th>\n",
       "      <td>-0.113879</td>\n",
       "      <td>-0.526794</td>\n",
       "      <td>-0.024521</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z5igARInaGBkjYdm</th>\n",
       "      <td>0.695860</td>\n",
       "      <td>5.780033</td>\n",
       "      <td>10.111497</td>\n",
       "      <td>3</td>\n",
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       "      <th>gH7nkfV2a0cKPQT6</th>\n",
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       "      <td>-0.392408</td>\n",
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       "      <th>wnYh1QfSNBcGMTJ5</th>\n",
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       "      <th>rL402wVDpGPBfC8q</th>\n",
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       "      <td>1.447394</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    R(标准化)    F(标准化)     M(标准化)  类别\n",
       "用户id                                               \n",
       "Gx4sJzcQog01UhZL -0.023908  0.173965   1.447321   0\n",
       "kEXrhTiug93DIcLG -0.023908  0.874723   1.447027   0\n",
       "AouXr0EOUtSRdiYK  0.605889  1.575482  -0.392408   0\n",
       "Yds7U30hnRZDiLtb  0.875802 -0.526794  -0.024521   1\n",
       "OFDTSXrhN9Q2mbVw -0.113879  7.181550   6.597663   3\n",
       "4qHSn3dkPzJTAjoG  0.335976 -0.526794  -0.539562   1\n",
       "tXkjbzpTsZcxYPKG  1.325658 -0.526794  -0.392408   1\n",
       "ro43b68MustgPyOR -0.203851  0.173965   0.711253   1\n",
       "18e2VC0IJ7SkcKzF  1.235687  0.173965   0.711253   1\n",
       "YCEo95zSZ08IJ3PW  0.066063  0.173965   0.122634   1\n",
       "cfGAgMWTJLt09VDs -0.293822  0.173965  -0.017163   1\n",
       "km0xTQGpIYZuXWyL  0.246005  0.874723   0.711253   0\n",
       "d9L37fAwopQkWPYh  0.335976 -0.526794  -0.392408   1\n",
       "UXewZPdJbBiycY8Q -0.113879 -0.526794  -0.024521   1\n",
       "Z5igARInaGBkjYdm  0.695860  5.780033  10.111497   3\n",
       "gH7nkfV2a0cKPQT6  1.235687 -0.526794  -0.392408   1\n",
       "wnYh1QfSNBcGMTJ5 -0.293822 -0.526794  -0.539562   2\n",
       "zPhEjLIWi3dKV6GU  0.066063 -0.526794  -0.024521   1\n",
       "JzrYtgsjF45bBun1 -0.023908  0.173965  -0.465985   1\n",
       "rL402wVDpGPBfC8q  0.965774  1.575482   1.447394   0"
      ]
     },
     "execution_count": 998,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KM_data = pd.concat([z_cdata,pd.Series(kmodel.labels_,index=z_cdata.index)], axis=1)\n",
    "KM_data.columns = list(z_cdata.columns) + ['类别']\n",
    "KM_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 999,
   "id": "4c62d4b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户id</th>\n",
       "      <th>消费金额</th>\n",
       "      <th>消费次数</th>\n",
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       "      <td>kEXrhTiug93DIcLG</td>\n",
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       "      <td>2016-06-19</td>\n",
       "      <td>31</td>\n",
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       "      <td>50.00</td>\n",
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       "      <td>Yds7U30hnRZDiLtb</td>\n",
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       "      <td>2016-06-16</td>\n",
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       "      <td>2016-06-27</td>\n",
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       "      <td>12.0</td>\n",
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       "      <td>2016-06-11</td>\n",
       "      <td>39</td>\n",
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       "      <th>7</th>\n",
       "      <td>ro43b68MustgPyOR</td>\n",
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       "      <td>22</td>\n",
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       "      <th>8</th>\n",
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       "      <td>2016-06-12</td>\n",
       "      <td>38</td>\n",
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       "      <td>200.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>2016-06-25</td>\n",
       "      <td>25</td>\n",
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       "      <td>120.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cfGAgMWTJLt09VDs</td>\n",
       "      <td>101.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>101.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>km0xTQGpIYZuXWyL</td>\n",
       "      <td>200.00</td>\n",
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       "      <td>2016-06-22</td>\n",
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       "      <td>UXewZPdJbBiycY8Q</td>\n",
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       "      <td>2016-06-27</td>\n",
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       "    <tr>\n",
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       "      <td>1477.60</td>\n",
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       "      <td>2016-06-18</td>\n",
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       "      <td>1477.60</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gH7nkfV2a0cKPQT6</td>\n",
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       "      <td>2016-06-12</td>\n",
       "      <td>38</td>\n",
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       "      <td>50.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>wnYh1QfSNBcGMTJ5</td>\n",
       "      <td>30.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>zPhEjLIWi3dKV6GU</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-25</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>2016-06-26</td>\n",
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       "      <td>4</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>35</td>\n",
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      "text/plain": [
       "                用户id     消费金额  消费次数   最后一次消费时间  R(最后一次消费距提数日时间)  F(月均消费次数)  \\\n",
       "0   Gx4sJzcQog01UhZL   300.04     2 2016-06-26               24        2.0   \n",
       "1   kEXrhTiug93DIcLG   300.00     3 2016-06-26               24        3.0   \n",
       "2   AouXr0EOUtSRdiYK    50.00     4 2016-06-19               31        4.0   \n",
       "3   Yds7U30hnRZDiLtb   100.00     1 2016-06-16               34        1.0   \n",
       "4   OFDTSXrhN9Q2mbVw  1000.03    12 2016-06-27               23       12.0   \n",
       "5   4qHSn3dkPzJTAjoG    30.00     1 2016-06-22               28        1.0   \n",
       "6   tXkjbzpTsZcxYPKG    50.00     1 2016-06-11               39        1.0   \n",
       "7   ro43b68MustgPyOR   200.00     2 2016-06-28               22        2.0   \n",
       "8   18e2VC0IJ7SkcKzF   200.00     2 2016-06-12               38        2.0   \n",
       "9   YCEo95zSZ08IJ3PW   120.00     2 2016-06-25               25        2.0   \n",
       "10  cfGAgMWTJLt09VDs   101.00     2 2016-06-29               21        2.0   \n",
       "11  km0xTQGpIYZuXWyL   200.00     3 2016-06-23               27        3.0   \n",
       "12  d9L37fAwopQkWPYh    50.00     1 2016-06-22               28        1.0   \n",
       "13  UXewZPdJbBiycY8Q   100.00     1 2016-06-27               23        1.0   \n",
       "14  Z5igARInaGBkjYdm  1477.60    10 2016-06-18               32       10.0   \n",
       "15  gH7nkfV2a0cKPQT6    50.00     1 2016-06-12               38        1.0   \n",
       "16  wnYh1QfSNBcGMTJ5    30.00     1 2016-06-29               21        1.0   \n",
       "17  zPhEjLIWi3dKV6GU   100.00     1 2016-06-25               25        1.0   \n",
       "18  JzrYtgsjF45bBun1    40.00     2 2016-06-26               24        2.0   \n",
       "19  rL402wVDpGPBfC8q   300.05     4 2016-06-15               35        4.0   \n",
       "\n",
       "    M(月均消费金额)  类别  \n",
       "0      300.04   0  \n",
       "1      300.00   0  \n",
       "2       50.00   0  \n",
       "3      100.00   1  \n",
       "4     1000.03   3  \n",
       "5       30.00   1  \n",
       "6       50.00   1  \n",
       "7      200.00   1  \n",
       "8      200.00   1  \n",
       "9      120.00   1  \n",
       "10     101.00   1  \n",
       "11     200.00   0  \n",
       "12      50.00   1  \n",
       "13     100.00   1  \n",
       "14    1477.60   3  \n",
       "15      50.00   1  \n",
       "16      30.00   2  \n",
       "17     100.00   1  \n",
       "18      40.00   1  \n",
       "19     300.05   0  "
      ]
     },
     "execution_count": 999,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.concat([data, pd.Series(kmodel.labels_,index=data.index)], axis=1)\n",
    "data1.columns = list(data.columns) + ['类别']\n",
    "data1.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1000,
   "id": "9a025018",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.569518</td>\n",
       "      <td>-0.178900</td>\n",
       "      <td>-0.120932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.068520</td>\n",
       "      <td>-0.556300</td>\n",
       "      <td>-0.401134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.290990</td>\n",
       "      <td>6.480792</td>\n",
       "      <td>8.354580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      R(标准化)    F(标准化)    M(标准化)\n",
       "类别                              \n",
       "0   0.429696  1.356495  0.801039\n",
       "1   0.569518 -0.178900 -0.120932\n",
       "2  -1.068520 -0.556300 -0.401134\n",
       "3   0.290990  6.480792  8.354580"
      ]
     },
     "execution_count": 1000,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_analysis = KM_data.groupby(KM_data['类别']).mean()\n",
    "kmeans_analysis.columns = ['R(标准化)', 'F(标准化)', 'M(标准化)']\n",
    "kmeans_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1001,
   "id": "ea3c8813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_analysis.plot(kind='bar', rot=0,yticks=range(-1, 9))\n",
    "plt.title('聚类结果统计柱状图')\n",
    "plt.xticks(range(0,3),['第0类','第1类', '第2类'])\n",
    "plt.grid(axis='y',color='grey', linestyle='--', alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1002,
   "id": "a327d0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans_analysis.plot(kind='bar', rot=0,yticks=range(-1, 9))\n",
    "plt.title('聚类结果统计柱状图')\n",
    "plt.xticks(range(0,4),['第0类','第1类', '第2类', '第3类'])\n",
    "plt.grid(axis='y',color='grey', linestyle='--', alpha=0.5)\n",
    "# plt.xlabel('类别1212')\n",
    "plt.ylabel('RFM三个指标均值')\n",
    "plt.savefig('聚类结果统计柱状图', dpi=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1003,
   "id": "a5cef85a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.42969582, 1.3564946 , 0.80103947])"
      ]
     },
     "execution_count": 1003,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "center_num = kmeans_analysis.values\n",
    "center_num[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1004,
   "id": "5a42314c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 4\n",
      "['R', 'F', 'M', '']\n",
      "4 4\n",
      "['R', 'F', 'M', '']\n",
      "4 4\n",
      "['R', 'F', 'M', '']\n",
      "4 4\n",
      "['R', 'F', 'M', '']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "# 绘图\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "# 设置为极坐标模式\n",
    "ax = fig.add_subplot(111, polar=True)\n",
    "feature = ['R','F','M']\n",
    "N = len(feature)\n",
    "feature.append('')\n",
    "for i, v in enumerate(center_num):\n",
    "    # 设置雷达图的角度，用于平分切开一个圆面\n",
    "    angles = np.linspace(0,2*np.pi,N,endpoint=False)\n",
    "    # 为了使雷达图一圈封闭起来，需要下面的步骤\n",
    "    center = np.concatenate((v[:],[v[0]]))\n",
    "    angles = np.concatenate((angles,[angles[0]]))\n",
    "    print(len(angles),len(center))\n",
    "    # 绘制折线图\n",
    "    ax.plot(angles,center,'o-', linewidth=2, label='第{}类'.format(i))\n",
    "    # 填充颜色\n",
    "    ax.fill(angles, center, alpha=0.25)\n",
    "    # 添加每个特征的标签\n",
    "    # ax.set_thetagrids(angles * 180/np.pi, feature,fontsize=15) # 报错原因是angles和feature长度不一致\n",
    "    ax.set_thetagrids(angles * 180/np.pi, feature,fontsize=15)\n",
    "    print(feature)\n",
    "    # 添加标题\n",
    "    plt.title(\"客户群特征分析图\",fontsize=20)\n",
    "    # 添加网格线\n",
    "    ax.grid()\n",
    "    # 设置图例\n",
    "    plt.legend(loc='upper right', bbox_to_anchor=(1.2, 1.0),shadow=True, fontsize=12)\n",
    "# 保存图形\n",
    "plt.savefig('客户群特征分析图', dpi=128, bbox_inches='tight')  # bbox_inches='tight'可以自动调整边距\n",
    "# 显示图形\n",
    "plt.show()\n",
    "\n",
    "# 问题：R没有显示出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1005,
   "id": "4af1c577",
   "metadata": {},
   "outputs": [],
   "source": [
    "KM_data['用户id'] = KM_data.index\n",
    "\n",
    "# 保存数据\n",
    "output_file_path = '类别-客户信息对应（标准化数据）.xlsx'\n",
    "KM_data.to_excel(output_file_path, index=False)\n",
    "\n",
    "output_file_path = '类别-客户信息对应（预处理数据）.xlsx'\n",
    "data1.to_excel(output_file_path, index=False)\n",
    "\n",
    "output_file_path = '聚类结果统计.xlsx'\n",
    "r.to_excel(output_file_path, index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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